Abstract
This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second sub-task, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline.- Anthology ID:
- 2021.dialdoc-1.8
- Volume:
- Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Song Feng, Siva Reddy, Malihe Alikhani, He He, Yangfeng Ji, Mohit Iyyer, Zhou Yu
- Venue:
- dialdoc
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 57–62
- Language:
- URL:
- https://aclanthology.org/2021.dialdoc-1.8
- DOI:
- 10.18653/v1/2021.dialdoc-1.8
- Cite (ACL):
- Nico Daheim, David Thulke, Christian Dugast, and Hermann Ney. 2021. Cascaded Span Extraction and Response Generation for Document-Grounded Dialog. In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021), pages 57–62, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cascaded Span Extraction and Response Generation for Document-Grounded Dialog (Daheim et al., dialdoc 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.dialdoc-1.8.pdf
- Code
- ndaheim/dialdoc-sharedtask-21
- Data
- Doc2Dial, doc2dial